>From: "Charles Goodwin" <[EMAIL PROTECTED]>
>Hi, I have just joined this list after seeing it mentioned on the Fabric of 
>Reality list....

    Hi.  BTW, what's up on the FOR list?  Ever see anything interesting 
there?  I thought the book sucked except for chapter 2 (I think; the one 
explaining the MWI), but at least there are some MWIers on that list I would 

>Would someone mind briefly explaining what FIN is (or at least what the 
>letters stand for)? Is it some version of QTI (Quantum
>theory of immortality) ?

    Yes, any version of "QTI" is FIN.

>Why should a typical observer find himself to be older than the apparent 
>lifetime of his species?

    I guess you mean "assuming FIN, why ..."

>so *very* few observeres are going to notice the TU versions of anyone 
>else. So the only way to actually experience this phenomenon is to live to 
>be that old yourself.

    Right ...

>I must ask, though, what makes you think that a typical observer ISN'T much 
>older than the lifetime of his species would allow?

    I'm not so old, but if FIN were true, the effective chance of me being 
old would be 100%.  So by Bayesian reasoning, it must be false.

>Given that you can't observe anyone but yourself in this state (or it's 
>"TU" that you ever will) (and I'm assuming you haven't reached 120 yet), 
>you can't really use a self-sampling argument on this, surely?

    On the contrary, you do use a SSA.  After all, you will never (for any 
question) have more than the one data point for use in the SSA.  But with a 
probability of 0% or 100%, that's plenty!

> >     It means - and I admit it does take a little thought here - _I want 
>to follow a guessing procedure that, in general, maximizes the fraction of 
>those people (who use that procedure) who get the right guess_.  (Why would 
>I want a more error-prone method?)  So I use Bayesian reasoning with the 
>best prior available, the uniform one on observer-moments, which maximizes 
>the fraction of observer-moments who guess right.  No soul-hopping in that 
>reasoning, I assure you.
>I'm sorry, I still don't see how that applies to me. If I know which 
>observer moments I'm in (e.g. I know how old I am) why should I
>reason as though I don't?

    Because you want to know things, don't you?  It's no different from any 
Bayesian reasoning, in that regard.
    Suppose you know that you just flipped a coin 10 times in a row, and it 
landed on heads all ten times.  Now you can apply Bayesian reasoning to 
guess whether it is a 2-headed coin, or a regular coin.  How to do it?

p(2-headed|got 10 heads) = [p(got 10 heads|2-headed) p_0(2-headed)] / N
p(1-headed|got 10 heads) = [p(got 10 heads|1-headed) p_0(1-headed)] / N

where N = p(got 10 heads) is the normalization factor so that these two 
conditional probabilities sum to 1 (they are the only possibilities).
    That's a standard use of Bayes' theorem.  But - whoa there - what's the 
p(got 10 heads) and the like?  You already _know_ you got 10 heads, so why 
not just set p(got 10 heads) to 1?
    Obviously, you consider the counterfactual case of (didn't get 10 heads) 
for a reason - that is, to help you guess something about the coin.  In the 
same way, the SSA helps you guess things.  It's just a procedure to follow 
which usually helps the people that use it to make correct guesses.

                         - - - - - - -
               Jacques Mallah ([EMAIL PROTECTED])
         Physicist  /  Many Worlder  /  Devil's Advocate
"I know what no one else knows" - 'Runaway Train', Soul Asylum
         My URL: http://hammer.prohosting.com/~mathmind/

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